Machine Learning Engineer, Applied AI

Role overview Build and operate machine learning systems that make everyday communication and coordination products more useful through AI. You’ll take practical ML work from model development through deployment, using real-world performance and user feedback to guide improvements.

Responsibilities - Develop and improve models for classification, retrieval, ranking, and personalization. - Raise search relevance with embeddings, hybrid retrieval, and reranking techniques. - Create evaluation pipelines, labeled datasets, and metrics to track model quality and catch regressions. - Maintain training, inference, and deployment pipelines; monitor models and investigate production issues. - Adapt existing models when data supports fine-tuning, balancing quality with latency, cost, and reliability. - Work with product and engineering partners to ship measurable improvements.

Requirements - Strong grounding in machine learning and modern neural architectures. - Experience deploying models into production, including work in classification, ranking, retrieval, recommendations, or personalization. - Understanding of evaluation methods, experiments, and performance metrics. - Ability to write production-quality code and work effectively with real-world datasets. - Judgment to balance model quality, latency, cost, and reliability. - Ownership, sound problem-solving, and ability to execute independently.

Nice to have - Experience with Python, PyTorch or JAX, embeddings, vector search, ranking models, or production inference systems. - Familiarity with privacy-aware personalization.